Increased Productivity, Role in Alleviating Food Insecurity Possible
Bibliographic record
Abstract
First paragraphs It's true that urban agriculture may provide a modest contribution to most cities' food supply. However, Hallsworth and Wong (2013) fail to recognize the range of cities across North America as well as the numerous opportunities to increase the productivity of urban agriculture and its potential role in alleviating food insecurity. They also under¬emphasize the value of urban agriculture beyond the quantity of food produced. There are many cities — Detroit, Cleveland, and Milwaukee come to mind — with large amounts of open space and notions of incorporating agriculture into the fabric of a 21st century green city. The authors fail to acknowledge the potential for expanded productivity per unit of land beyond what is currently observed, for example with the use of passive solar, season-extension methods. In Michigan, with average low temperatures below Vancouver's, unheated hoophouses allow for at least 30 crops to be grown, many year-round (Colasanti, Matts, Blackburn, Corrin, & Hausler, 2010). The authors dismiss what can be grown in a 4-square-meter (43-square-feet) garden as "suitable only for... personal enjoyment," but during the frost-free period an extra vegetable serving for a family of four per day is easily accomplished in this space....
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".